On the Design of Quantization Functions for Uplink Massive MIMO with Low-Resolution ADCs

Quantization is the characterization of analogue-to-digital converters (ADC) in massive MIMO systems. The design of quantization function or quantization thresholds is found to relate to quantization step, which is the factor that adapts with the changing of transmit power and noise variance. With the objective of utilizing low-resolution ADC is reducing the cost of massive MIMO, we propose an idea as if it is necessary to have adaptive-threshold quantization function. It is found that when maximum-likelihood (ML) is employed as the detection method, having quantization thresholds fixed for low-resolution ADCs will not cause significant performance loss. Moreover, such fixed-threshold quantization function does not require any information of signal power which can reduce the hardware cost of ADCs. Simulations have been carried out in this paper to make comparisons between fixed-threshold and adaptive-threshold quantization regarding various factors.

I. Introduction

Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for 5G communications; by using tens of antennas at the base station, throughput and reliability will be largely enhanced in communication system [1]. But this technology still faces a lot of technical challenges, for example, the huge demand of the antennas will lead to the requirement of a large number of RF chains, and this will result in the increase of the hardware complexity and hardware cost [2]. It is known that analogue-to-digital converter (ADC) contributes the most to the RF chain power consumption with the consumption increases exponentially with resolutions [3]; based on this background, employing low-resolution (1, 2, 3 bit) ADCs at the receiver side is recognized as one of the cost-effective schemes for future wireless communications. This technology has already attracted many and still increasing research efforts, mainly towards signal detection, channel estimation, as well as performance evaluation [4]–[7].